ETFC: Energy-Efficient and Deadline-Aware Task Scheduling in Fog Computing

Abstract

The Internet of Things (IoT) is constantly evolving and expanding. However, due to the limited IoT resources, it is intertwined with fog computing to use their resources to compensate for the limitations of IoT resources. On the other hand, fog devices face challenges, such as resource heterogeneity, high distribution, dynamism, and limitations, so an efficient task scheduling approach is needed to deploy fog computing resources effectively and improve the quality of service (QoS). This work mathematically formulates the task scheduling problem to minimize energy consumption and cost and improve QoS by reducing response time and deadline violation times of IoT tasks. Then, it proposes an Energy-efficient and deadline-Aware Task scheduling in Fog Computing (ETFC) method that predicts the traffic of fog nodes by a Support Vector Machine (SVM) and divides them into low-traffic and high-traffic groups. Next, the ETFC method schedules the low-traffic part with an algorithm based on reinforcement learning using the proposed ICLA-SOA, which is an algorithm based on irregular cellular learning automata and schedules the tasks of the high-traffic part with a metaheuristic algorithm using the proposed Non-dominated Sorting Genetic Algorithm (NSGA-III). The simulation results demonstrate that the ETFC method exhibits up to an 84% enhancement in response time, up to a 33% reduction in energy consumption, up to a 30% decrease in costs, and up to a 28% advancement in meeting task deadlines compared to other methods.